Business planner

ABSTRACT

A method and system for predicting the profit attributable to a proposed sales promotion of a product, wherein the product has a wholesale price and a manufacturing cost per unit sales, including establishing a base volume for sales of the product in the absence of promotions; determining a sales lift for the plurality of single promotions; and correlating the sales lift with promotion information to provide a sales lift model. The method and system also include proposing a promotion having a cost per unit sales for a promotion time period and having a planned sale price for the product; applying the sales lift model to the proposed promotion to predict sales of the product for the promotion time period; and calculating manufacturer profit based upon the product&#39;s predicted sales, cost per unit sales for promotion, wholesale price, and manufacturing cost per unit sales during the promotion time period.

CROSS-REFERENCE TO RELATED APPLICATION

This application claims the benefit of U.S. Provisional Application No.60/336,564, filed Dec. 4, 2001, herein incorporated by reference.

BACKGROUND

In marketing a product across many geographical regions or throughmultiple retailers, multiple teams of sales personnel are typicallyinvolved. Each team may establish their own program for promotionalactivities (coupons, temporary price reductions, etc.) with the retailerfor which they are responsible or for the market or region theysupervise. Experience may be a guide as to the impact a particularpromotion may have on product sales, allowing sales staff to estimate,for example, that a 10% price reduction may result in a 50% increase insales.

SUMMARY OF THE INVENTION

Accurate models, however, have not been available for evaluatingmultiple proposed promotion plans in terms of sales increase andprofitability. In fact, promotional plans in many cases are solely orprimarily focused on increasing sales volume, and frequently areexecuted without direct consideration of profitability. This is due, inpart, to the lack of useful tools for planning and assessingprofitability of promotions.

Salespersons do not have access to a planning system that allows them tocompare multiple promotional scenarios, or that allows retailers tounderstand the impact on sales and profits of the promotions beingconsidered. There has been a long-standing need for a reliable means forestimating the return on investment (ROI) for a promotion such as acoupon campaign or a two-for-one sale. There has also been a need forcontemplated promotional plans to be tied to production plans and/ormarketing objectives of the manufacturer. An integrated system of tyingpromotion plans and predicted sales results from multiple regions ormarkets to corporate business plans appears to have been lacking in thepast. Further, there has been a need for a system that may integratewidespread promotion and production plans, particularly on aninternational level, to ensure that business plans effectively fulfillcorporate objectives.

A computerized business planner system has been developed to allow salesstaff and, optionally, retail personnel at multiple locations to plansales promotions for specific products in a manner that may be tied tomanufacturer production and marketing plans, and in a manner thatpermits estimation of the increased consumer sales volume due to thepromotions. The system optionally includes an estimation of profit toboth manufacturer and retailer. The business planner system provides anexploratory decision support and planning tool for sales staff and theirretail associates. The business planner system may also link andintegrate planned promotions from multiple sites to production plans ofthe manufacturer, so that planned production may be in line withprojected increases in sales, or so that promotion plans may beiteratively adjusted to comply with marketing objectives or productionplans, including production limitations and other factors in the supplychain of the manufacturer.

More specifically, the scope of the present invention includes a methodand system for predicting the profit attributable to a proposed salespromotion of a product, wherein the product has a wholesale price and amanufacturing cost per unit sales, including establishing a base volumefor sales of the product in the absence of promotions; determining asales lift for the plurality of single promotions; and correlating thesales lift with promotion information (e.g., the promotion type anddiscount value) to provide a sales lift model. The method and systemalso include proposing a promotion having a cost per unit sales for apromotion time period and having a planned sale price for the product;applying the sales lift model to the proposed promotion to predict salesof the product for the promotion time period; and calculatingmanufacturer profit based upon the product's predicted sales, cost perunit sales for promotion, wholesale price, and manufacturing cost perunit sales during the promotion time period.

Other objects and advantages of the present invention will become moreapparent to those skilled in the art in view of the followingdescription and the accompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a flowchart illustrating several features of a businessplanner system.

FIG. 2 is a flowchart illustrating additional features of the businessplanner system of FIG. 1.

FIG. 3 is a flowchart illustrating additional features of the businessplanner system of FIG. 1.

FIG. 4 is a flowchart illustrating several promotion and productionplanning features of the business planning system of FIG. 1.

FIG. 5 is a flowchart illustrating several sales lift features of thebusiness planning system of FIG. 1.

FIG. 6 is a flowchart illustrating several promotion planning featuresof the business planning system of FIG. 1.

FIG. 7 is a schematic representation of the hardware for a computernetwork that may be used for the business planner system of FIG. 1.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

As used herein, “supply chain” refers to the chain of events fromacquisition of raw materials to the production of goods and theirdistribution to retail environments. The supply chain for a product mayinvolve obtaining raw materials for manufacture, use of the rawmaterials in a production setting to produce the product, warehousingthe product if applicable, shipping of the product to a retailer orintermediate distributor or warehouse, followed by whatever additionalsteps are needed to place the product into control of the retailer.Information pertaining to supply chain management may pertain to millinformation about planned and current production, warehousinginformation, shipping plans, and so forth, and may include projecteddemand and shipping needs and other factors that may affect theavailability of a product at a particular time.

As used herein, “lift” refers to the increase in sales volume caused bya promotion. For example, a temporary price reduction of 10% during aone-week period might result in a 50% increase in sales during thatperiod.

As used herein, “lift model” refers to a mathematical model forpredicting the lift in consumer sales of a product or the increase insales for a related product category for a specific customer (or groupof customers), apparently caused by a product promotion, obtained bycorrelation of past sales performance in light of contemporarypromotions and base volume, or by any other suitable technique.

As used herein, “promotion” refers to a temporary action taken toincrease consumer sales of a product during the time period in which thepromotion is offered. Any type of promotion may be considered. Examplesof well-known promotion types include the following:

-   -   1. A Temporary Price Reduction (TPR), wherein a retailer offers        a product at a reduced price.    -   2. Rewards for multiple purchases, such as a “buy two, get one        free” promotion.    -   3. Commercial advertising, especially at an increased level        relative to normal practice to cause a temporary increase in        sales, either with or without an advertised reduction in price        or other tangible incentive to purchase. Advertising may be used        in combination with any other promotion, and may be in any        suitable medium. This promotion includes other suitable        promotion methods, or combinations thereof. Advertising may be        in any known medium, such as print, billboards, Internet ads,        television, radio, and the like.    -   4. Special merchandise treatments by using various display types        such as power walls, gondola ends, and floor stacks.    -   5. Loyalty card promotions, including smart card transactions,        wherein the purchaser receives a discount when the product is        purchased and a loyalty card is used.    -   6. Coupon promotions offering consumers cents off for single or        multiple purchases with presentation of the coupon at time of        purchase.    -   7. Temporary increased value systems, wherein higher value is        temporarily offered, such as offering a larger quantity of        product or another prize or product or coupon provided with the        product being purchased, while the price of the product itself        may be unchanged.    -   8. Sweepstakes and related programs wherein the purchaser of a        product is eligible to win a prize, including instant winner        promotions wherein a hidden message on or in the product that is        revealed after purchase indicates whether the purchaser has won.    -   9. Rebate programs, including mail-in rebate systems and instant        rebates, wherein the purchaser is offered cash back immediately        or shortly after purchase of the product.    -   10. Media, sampling, coupon, or other broadcast promotions that        encourage the consumer to purchase products in addition to        discounts and merchandising offered by retailers. In this case,        the base volume for the product in the promotion period may be        increased beyond the additional incremental lift caused by        retailer based promotions.

The promotion information pertaining to a particular promotion type mayinclude the promotion type, the level of discount or other quantifiedmeasure of the offered incentive (e.g., sweepstakes grand prize amount,expected winnings per customer for a sweepstakes, approximate cash valueof non-cash incentives, etc.), cost of the promotion (e.g., cost percapita or cost per consumer of advertising, cost of retailer fees paidby the vendor plus discount cost, etc.), and the like. Such informationcan be used to correlate past sales performance for a product topromotion activity. In correlations or other analyses performed on thepromotion information, the promotion information can be treated at anydesired level of detail. For example, past promotions of a single typebut with varying discount levels may be treated separately or groupedtogether.

As used herein, “promotion time period” refers to the time that apromotion is in effect to the consumer. During this period some type ofpricing discount, new or increased consumer incentive, or merchandisingactivity must exist to generate incremental consumer demand for theproduct. The promotion time period may range from one day to eight weeksor longer. Longer time periods may be handled, if desired, through theuse of back-to-back promotion periods.

As used herein, “planning interval” refers to a period of time overwhich business and promotion plans are made and integrated. For example,a six-month period is commonly used for calendaring and managementforecasting purposes, and serves as a suitable planning interval. Otheruseful planning intervals may be but are not limited to any of thefollowing, which are offered by way of illustration only: 2 months, 3months, 4 months, 8 months, 12 months, 18 months, and 24 months.

As used herein, “base volume” refers to sales volume for a market ormarket site that occurs or would occur in the absence of short-termpromotions. Base volume may increase or decrease over time due tochanges in seasonality, demographics, competitive products, consumerdemand, and so forth. Base volume may be estimated with fair accuracyusing point-of-sale scanner data for periods free of promotions, butscanner data is not always available or affordable. There is a need toestimate base volume from shipping data, but the volume of a productthat is shipped may be influenced by many factors that obscure basevolume. Prior methods for estimating base volume are based largely onmanual estimates obtained from plots of shipped volume over time, inwhich peaks corresponding to promotions are at least partially ignored.Automated methods based on peak truncation and smoothing of curves failto provide reliable estimates and leave the user with no estimate of theaccuracy of the prediction. Estimation of base volume in light ofdramatically large peaks of sales volume obscures the actual volume soldor the base volume.

To strengthen the predictive abilities of the business planner system,improved robust methods for base volume determination have beendeveloped. These methods not only provide more accurate estimates thanwere previously possible, but also provide estimates of the accuracy ofthe base volume prediction. With the improved methods, scanner data doesnot need to be purchased to determine base volume with a known amount ofaccuracy.

In general terms, the robust method includes the following steps:

-   -   a) Obtaining shipping data over time;    -   b) Normalizing the data;    -   c) Truncating large peaks associated with promotions;    -   d) Filtering or smoothing the data, preferably with a dynamic        linear model (or Kalman filter) applied to the series data first        in one direction and then in the reverse direction;    -   e) Performing regression on the data, preferably a        non-parametric regression such as support vector regression, to        obtain regression estimates;    -   f) Smoothing the regression estimates;    -   g) Determining the base volume for any period of time as the        mean of the regression estimates for that period.

Non-parametric regression offers advantages over parametric regressionin that the estimates are more robust for the purposes of the presentinvention. Support vector regression, a recent development in regressionpractice, allows the user to minimize the risk of the prediction toachieve a specified acceptable level of error.

As used herein, “promotion cross effects and drop off” is the activityof reducing base volume on related products or on the same product afterthe promotion has ended to reflect consumer switching during promotionperiods and household pantry loading when promotions offer significantshort term incentives that cause such consumer behaviors. For example, aconsumer may not need to purchase a product immediately following apromotion because that person took advantage of the promotion andpurchased more than was required during the promotion.

As used herein, “product” refers to any article that can be marketed andsold, and may include any article known to be sold in a retailenvironment such as a supermarket, specialty shop, mall, or the like.Products can also refer to wholesale goods or goods distributed throughchannels other than retail stores, such as medical supplies, items soldvia e-commerce or by Internet distributors, business-to-businessproducts, and the like. Products may include items manufactured infactories, made by hand, grown on a farm, harvested from plants oranimals, mined or extracted from natural resources, and the like. Inmany cases, products are produced or marketed by a vendor and sold tocustomers by a distributor, which may be a retailer operating in aretail environment.

As used herein, a “product category” refers to a set of related articlesthat are grouped together for consolidation of results. For example, acompany offering premium diapers in three sizes may refer to the premiumdiaper category. Combining regular diapers of the same brand name withpremium diapers may yield a broader brand-name category. Combining thebrand-name diapers with diapers sold under third-party names but stillmade by the same manufacturer may yield a general diaper category.Combining diaper sales information with combined feminine care productsales and incontinence product sales may yield a large absorbent articlecategory.

As used herein, a “market” refers a segment of the economy based on afactor such as location, income, age, product need, distributor type,and so forth. Thus, absorbent articles may be sold to a Utah market, ateenage market, a vending machine market, and so forth. A “market site”refers to a specific subset of the market having a specific retailersuch as a chain of store or a single store.

As used herein, the “manufacturer” is normally taken to be the entity(or collection of entities) that produces the product or products to bedistributed to a retailer. It is understood that agencies representingthe manufacturer may also oversee the distribution of product to theretailer and be encompassed in the term manufacturer without necessarilymanufacturing the product. Therefore, as used herein, manufacturer isgenerally synonymous with vendor.

As used herein, “customer” refers to a distributor (e.g., a retailer) orsimilar entity that purchases products from a manufacturer or adistributor.

As used herein, “consumer” refers to a person, business, or similarentity that purchases products on the retail market from a retailer orsimilar entity.

The business planner system is a computerized tool that acceptsassumptions for arbitrary scenarios and permits prediction of theeffects likely to be realized by a planned or prophetically consideredpromotion for one or more products or product classes in one or moremarkets and/or for one or more distributors. Predicted effects mayinclude at least one of consumer sales lift, expected ship quantitiesand timing, retailer profit or revenue, and manufacturer profit orrevenue. Using a business planner system, business plans from multiplemarkets may be scheduled, integrated, and tied to corporate productionand marketing plans, allowing production and marketing for future timeintervals to be planned in detail, with estimates of profitability.

A business planner system may be hosted on a central server accessibleremotely by other computers over a network, or may be divided intointeracting modules hosted on a plurality of computers, includingcentral manufacturer servers, retailer servers, and sales staff personalcomputers.

Electronic Data Interchange (EDI), which refers to computer-to-computerelectronic exchange of machine-processible business data documents, maybe implemented in any of several forms, and may be used for theelectronic integration of local sales staff marketing plans withmanufacturer data and projections for sales, operations, prices, anddistribution. An EDI system may add further links or interfaces to tieinto retailer sales plans, profitability estimates, shelf spaceallocation proposals, transaction systems, and the like. An exemplarytransaction EDI system is the SAP R/3 system. Principles of implementinga SAP R/3 system are described in the following references:

-   -   Successful SAP R3 Implementation: Practical Management of ERP        Projects by Norbert Welti, New York: Addison-Wesley Pub Co.,        1999 (ISBN: 0201398249).    -   SAP R/3 Implementation Guide: A Manager's Guide to Understanding        Sap by Bradley D. Hiquet, Kelley-Levey, Associates, and        Anthony F. Kelly, Indianapolis: Macmillan Technical Publishing,        1998 (ISBN: 1578700639).    -   SAP R/3 Process Oriented Implementation: Iterative Process        Prototyping by Gerhard Keller, Thomas Teufel, New York:        Addison-Wesley Pub Co., 1998 (ISBN: 0201924706).

In one embodiment, the business planner system offers a Web interface topermit a retailer or remote sales staff member to experiment with avariety of scenarios to determine the benefits of alternate promotions.A user may access the business planner system or needed manufacturerdatabases using a Web browser on a personal computer or other computerconnected via the Internet or other network to a server owned orcontrolled by the manufacturer. Access to certain aspects of thebusiness planner system or certain databases may be restricted based onthe identity of the user, who may be required to log on with a usernameand password or who may be identified with biometrics such as thumbscan, retina scan, voice matching, etc. Retailers, for example, may beprovided a lower level of access than employees of the manufacturer.

One embodiment showing several features of the business planner system 1is illustrated in FIG. 1. Sales staff representing or employed by themanufacturer are shown for one division, such as a geographical regionor a particular chain of stores. Sales staff in the division developpromotion plans 2 for a given time period, such as a three- or six-monthinterval.

Proposed promotions are entered into a computer program that runs a liftmodel 4 for the products and markets of concern in the division.Prediction of increased sales, or sales lift, due to a promotion isachieved using mathematical models for market response to a set ofpromotion conditions, with a plurality of promotion types beingavailable in the model. The models may be derived from correlations ofpoint-of-sale (POS) data with past promotions and, in some embodiments,may be updated continuously as POS data is obtained for each promotion.Information Resources, Inc. (IRI) (Chicago, Ill.) and A. C. Nielsen(Stamford, Conn.) are examples of commercial sources of householdproduct data and POS data in particular, and both have the ability toprovide lift models for a given product. Commercially-provided liftmodels may be used, or in-house models may be developed from availablePOS data or other data using suitable techniques for regression ormultivariate analysis. For example, the PROMOCAST-brand forecasting toolfrom the University of California at Los Angeles may be used. This toolis believed to be offered commercially by ems (Efficient MarketServices, Inc.) (Deerfield, Ill.). This tool uses historical databasesof sales for a variety of promotion conditions at specific retailers andapplies a 67-variable regression model to predict how a plannedpromotion will affect sales in a particular store. The tool is describedby Lee G. Cooper et al. in “PromoCast™: A New Forecasting Method forPromotion Planning,” Marketing Science, Vol. 18, No. 3, 1999, pp.301-16, incorporated herein by reference. The PromoProphet™ system fromIRI may also be used, as described by R. Margulis in Retail Info SystemsNews, June/July 1998, at www.risnews.com/archive/June98_(—)8.shtml.

The lift model may use a predicted base volume for the division asfurther input. The sales, sales lift, and profit are then computed 6,based on the lift model. These predicted results 6 are compared 8 withmarketing objectives 10 and predicted capacity 12 for the product inquestion to see if the sales expected to occur in light of promotionplans are aligned 14 with corporate plans and objectives. If thepredicted results are in alignment, then the promotion may proceed tocompletion 16. If the predicted results are not in alignment, then adecision 18 is made whether to modify the promotion plans or operationplans, with consideration of marketing objectives 10. Adjustments to thepromotion or operation plans 2 are then made, as appropriate, and themodified plans are iterated through the process.

Similar predictions may be made for multiple divisions (not shown), witheach division applying a lift model 4 to predict sales. Predicted sales6 may then be integrated for comparison 8 with predicted capacity 12 orother production plans, and may be integrated or treated separately foreach division for comparison to marketing objectives 10.

An embodiment showing additional features of the business planner system1 is illustrated in FIG. 2. Sales staff representing or employed by themanufacturer are shown for three regions 20, 22, and 24. Sales staff ineach region develop promotion plans for a given time period, such as athree- or six-month interval. For example, sales staff in Region 1 maypropose a direct mail coupon promotion offering 50 cents off each unitof the product, where the coupons are to be distributed on a particulardate. Sales staff in Region 2 may propose a temporary price reductionwith two retailers for overlapping periods of time. Finally, sales staffin Region 3 may propose a 60 cent price reduction for loyalty card usersof a single retailer. Proposed promotions from each of the sales staffgroups are supplied over a network to the manufacturer, and specificallyto a server owned or controlled by the manufacturer, where thepromotions are integrated 26, or rolled up, to provide the manufacturerwith the combined plans from a plurality of sales regions where theproduct or product category is sold.

The plurality of sales regions may represent any fraction of the market,such as the entire market for the product, whether the market is global,in a specific country, or in a geographical region. The plurality ofsales regions may also represent a majority of the total sales volumefor the product, such that the combined sales regions may be expected toaccount for at least about 50% of the total sales of the product, morespecifically at least about 70% of the total sales of the product, andmore specifically about 90% of the total sales of the product.

The promotions for each region are linked to the sales lift model 4,allowing model predictions 6 to be calculated. Application of the saleslift model generally includes predicting the sales lift separately foreach region based on regional plans and predicted regional base volume,though predictions may be done at an aggregate level if desired or forindividual stores or groups of stores for specific retailers. Basevolume is predicted 28 based on POS data 30 and shipping data 32 for theproduct. POS data 30 is calculated from a retail sales database for eachregion including data from retail sales in that region. In the exampleshown in FIG. 2, three regions 34, 36, and 38 are considered. Shippingdata 32 is calculated based on data from a manufacturer shippingdatabase 40, which also obtains data from an operations database 42.

Sales data from various regions may be used to establish the respectivebase volume predictions 28 for each division, or a collective basevolume. When POS data 28 or other reliable sales information is notavailable or is too costly, shipping data 32 may be used to obtain anestimate of base volume, though filtering and smoothing of sales datamay be needed to estimate the true base volume. One source of basevolume data is the Efficient Market Services, Inc. (Deerfield, Ill.),which uses the “ems algorithm” based on weighting of sales yesterday, aweek ago, and eight days ago, if those days were non-promotion days forthe product in question. For a short time period, base volume may beestimated from consumer sales data in the time period immediatelypreceding a promotion. For longer periods of time such as those, forexample, greater than a month, base volume should be estimated throughconsideration of population trends and other factors as well, when suchdata are available. In the absence of POS data 28, base volume may beestimated from shipping volume records using a base volume calculator,as described herein. Of course, any suitable method for calculating basevolume may be used.

Predicted sales, sales lift, and profits to the manufacturer and/orretailers are calculated 6, generally at a regional or store-basedlevel, followed by integration of the regional or store-based results(not shown), and then compared 8 to marketing objectives 10, predictedcapacity 12 and other business objectives or constraints, includingproduction plans and constraints available from the operations database42.

If the predicted sales and profit levels 6 are in alignment 14 withmarketing objectives 10, manufacturing capacity 12, or other constraintsor objectives, the promotion may proceed to completion 16. If thepredicted sales and profit levels 6 are out of alignment with marketingobjectives 10, manufacturing capacity 12, or other constraints orobjectives, the sales staff in one or more regions receives instructionsto bring the predicted sales and profits in line with corporate needs18. For example, if the sales lift model predicts that the sales volumewill be too low, one or more of the sales regions will be asked toincrease the level of promotion to increase sales, such as by offering ahigher discount or by increasing advertising. The request for amodification in sales plans may be done manually, or a computerizedmeans may suggest the region or combination of regions with the greatestpotential for low-cost modifications to bring the integrated plans inalignment with corporate objectives. One or more new promotions willthen be planned and considered in the sales lift model to bringpredicted sales and profits better into alignment with corporateobjectives and constraints. If alignment may not be achieved or may notbe achieved without excessive costs, changes may need to be made inmarketing objectives 10 or in manufacturing plans.

In any case, the sales lift model 4, and integrating promotion plansfrom multiple regions 26, allow the manufacturer to predict future salesand profits and to bring promotions and marketing objectives intoalignment in an interactive manner. As shown in FIG. 3, sales staffmembers 44 propose promotions 46 that are adjusted to meet marketingobjectives 10. Proposed promotions 46 are analyzed with predictivemodels 48 such as the sales lift model to predict profitability, salesvolume, and so forth, and the predicted information is used to show thebenefit to retailer 50 and obtain their approval or support 52 for theproposed promotions 46, which are then implemented 54.

Sales data during promotions may also be used to continuously improvethe sales lift models by correlating actual sales lift with details ofthe promotions being run. Demographics of the store and region may alsobe considered in the sales lift model.

The system further comprises optional tools for dynamic pricing, whereinthe price to be paid by the retailer for the goods is a variabledependent on one or more factors such as time, projected demand,projected raw materials costs, and so forth. In one embodiment, the unitprice paid by the retailer for use in a model predicting retailer profitis a time-dependent variable whose value at a given time is projected bythe manufacturer based on forecasts of material costs and other factors,and is available for use in the model of profitability by accessing amanufacturer database of projected price to the vendor. The price mayalso be offered by the manufacturer in a database as a function ofretailer volume, incorporating volume discounts, for example. In anotherembodiment, the price offered by the manufacturer in the databasedepends on the specific retailer, incorporating existing contractualarrangements regarding pricing. The price may also be offered as atentative prediction responsive to input from the retailer.

It is possible for a retailer or vendor to input certain promotionparameters such as volume expectations, profit limits, etc., and allowthe business planner system 1 to generate promotion scenarios that meetthese requirements using models and possible promotion conditions. Inthis case, the retailer or vendor may then select a scenario and applyit to the business planning system. In essence, this requires adaptingthe software to provide an inverse solution, wherein the desiredsolution (financial returns or sales lift, for example) is provided asinput, followed by a computer-assisted search for promotion conditionsthat may yield the solution. In some cases, a nearly infinitecombination of conditions, such as combinations of sales price discount,retailer payout to the customer, coupon promotions, etc., may achievethe targets. The software may then identify the parameter space capableof providing the solution or offer a range of conditions for furtherexamination, or request additional restraints on the solutions, such asthe type of promotion, the maximum discount allowed, etc.

In another embodiment, the business planner system 1 is integrated witha transactional system such as the SAP R3 system for dynamic pricingcapabilities, wherein the manufacturer cost of the goods underconsideration is a variable that depends in part on forecasted rawmaterial costs, expected market demand, predicted transportation costs,regulatory costs, and so forth. The planned wholesale price of the goodsfor future dates is also a dynamic variable depending on businessobjectives, predicted demand, and product cost. Thus, predictions aboutfuture promotions may take into account available information from aplurality of sources that will affect future costs and future prices.The retailer using a business planner system Web site may only accessinformation reflecting future wholesale prices and suggested retailprices, while sales staff or other agents of the manufacturer may haveaccess to projected production costs and other costs, as well as plannedwholesale prices and suggested retail prices.

The business planner system 1 may also be adapted to handle the forwardbuying behavior of customers. Forward buying is the practice employed bysome retailers of stockpiling large quantities of a product when it isoffered at a lower-than-normal price by the manufacturer during apromotion, allowing them to sell the stockpiled product at the normalprice after the promotion, thus making additional profit. This is notdesirable from the perspective of the manufacturer because the costincurred for offering the goods at a reduced price is not used toincrease consumer demand. Past forward buying behavior of a customer maybe indicative of general practices, so the business planner system 1 mayinclude a module that tracks past forward buying behavior andextrapolates to estimate the forward buying actions of the customer fora given planned promotion. The business planner system 1 may theninclude the anticipated cost of forward buying for a particular customerinto the net costs to the manufacturer of the promotion. If desired,this information may then be applied by the manufacturer to negotiate afavorable alternative to forward buying with the customer. For example,the manufacturer may show the customer what their expected forwardbuying behavior is and what the expected profit to the customer is byforward buying, and then offer an alternative that provides the sameprofit to the customer but which also increases consumer demand ormanufacturer profit. For example, the manufacturer may offer anadditional or extended promotion or additional pay out to the vendor.

The business planner system 1 has two planning modes. The full modeallows planning from a consumer purchase perspective and builds shipmentestimates and supply chain demand. Under this mode a complete set ofretail and vendor financial figures are available for follow-upevaluation. The second mode referred to as “Lite” allows the planner toinput shipment estimates and the business planner system 1 willoptionally project consumer demand. In this mode, only vendor financialfacts are present and no retail pricing, merchandising, or modeling isavailable. This mode presents an alternate planning mode often used forwholesalers or small retail customers.

Shipping practices, year-end practices, and other factors may result inintermittent demand rather than steady demand. To better handleintermittent demand, the business planner system 1 may incorporatesuitable systems for forecasting intermittent demand, such as thosedisclosed in U.S. Pat. No. 6,205,431, “System and Method for ForecastingIntermittent Demand,” issued Mar. 20, 2001 to T. R. Willeman and C. N.Smart.

Transactions involving multiple currencies may be handled with suitablemeans, such as those disclosed in U.S. Pat. No. 6,205,433, “System andMethod for Multi-currency Transactions,” issued Mar. 20, 2001 to B. P.Boesch et al. Methods for handling payment and purchase transactions mayalso be handled with any suitable method, such as that of U.S. Pat. No.6,205,437, “Open Network Payment System for Providing Real-TimeAuthorization of Payment and Purchase Transactions,” issued Mar. 20,2001 to D. K. Gifford.

Data information from multiple sales staff and/or multiple vendors orfrom other sources may be integrated using any suitable method, such asthat of U.S. Pat. No. 6,205,446, “Method for Merging Multiple KnowledgeBases into One Optimized and Compressed Knowledge Base,” issued Mar. 20,2001 to S. Mittak and S. S. Khedkar.

The roles of the business planner system 1 in the successful schedulingof promotions and production levels are shown in FIG. 4. The businessplanner system 1 is used to predict the outcomes of various eventscenarios 56, from which the most favorable scenarios are selected. Liftmodels 4 and cost factors 58 are used to predict profitability toretailers and/or the manufacturer. The results may be shown to themanufacturer in a manufacturer view 60, with access to all informationand to results from multiple regions and retailers. The results may alsobe shown to individual retailers in a retailer view 62, with access toresults predicted for the particular retailer in question. These viewsmay be presented on a computer screen with a Web browser or customsoftware. The selected scenarios are used to create a promotion plan forthe manufacturer, shown here as a six-month promotion plan 64, and maybe integrated with the retailer promotion calendar 66 of each respectiveretailer. The event scenario 56 may also be aligned with other aspectsof the retailer promotion calendar 66. For example, if the retailer isplanning a major promotion of a competitive product during one timeperiod, the manufacturer may need to adjust the timing of a plannedpromotion accordingly. Also, it may not be in the best interest of aretailer to participate in two competitive promotions at the same time.Iterative planning may be needed to plan promotions that align with theneeds and plans of the retailers as well as the manufacturer. In oneembodiment, the business planner system 1 includes calendaring modulesthat allow integrated promotion plans to be graphically displayed and tocompute the impact of modifications to the plans.

FIG. 5 illustrates one aspect of the continuously refinable andinteractive nature of the sales lift models 4. As promotions areproposed 68 by the vendor 70 and a retailer 50, and implemented 72 bythe retailer 50, in light of predicted sales lift 74 and profitability,actual sales data may be tracked, for example, via scanning at check outto provide POS data 76. The actual sales data may then be used tocalculate actual sales lift, which may then be correlated with thepromotion to further refine the lift model or models 78 used to predictthe outcome of promotions 74. Future predictions are thus enhanced byregular analysis of POS data 76.

As shown in FIG. 6, the business planner system 1 may also include meansfor tracking, managing, and allocating funds for promotions. Promotionalplans and calendars from multiple retailers 80, 82, and 84 may beentered by the respective sales staff 44 in modules of the businessplanner to create an integrated promotions calendar 86 on a centralserver hosting the centralized business planner software 88, permittingpredictions of costs and profits 90 to be made. The projected costs forthe planned promotions may then be reviewed by management and approved92 or iteratively revised (not shown). For example, after a salesrepresentative 44 for the manufacturer has entered preliminarypromotional plans in the business planner system 1 to create apromotional calendar for a retailer 80, the business planner system 1may compute the funds required for the plan, or the funds may beestimated manually and entered by the sales representative. Themanufacturer may then review integrated plans from a plurality of salesrepresentatives to determine the total funds required. If the requiredfunds are excessive, directives to modify the planned promotions may beissued to the sales representatives. Otherwise, the plans may beapproved and funds allocated 94 for the promotions. As the actual costsaccumulate either by paying bills or through off-invoice allowances, thefunds information is updated to track actual costs. The actual costs maybe used for plan evaluation to improve the return on investment forsuccessive promotions. For example, formulas predicting the cost of apromotion may be updated based on the actual costs of a promotion forbetter planning in the future.

The system may also provide a means to transfer funds, file claims, anddirectly pay customer banks, if desired.

The system may further be used with shelf-space management systems, suchas the MARKETMAX-brand Planogram Manager, whereby planned promotionsalso include information pertaining to adjustments in shelf-spacearrangements during the promotion.

Numerous computer models for allocation of shelf-space and estimation ofthe economic impact of particular shelf-space arrangements have beenreported in the literature, and any of these may be implemented as partof the business planner system 1 to allow the vendor and/or retailer toconsider the effect of shelf-space allocation on the projected sales,including during an active promotion or in the calculation of baselines.Exemplary models include that of Timothy L. Urban, “AnInventory-Theoretic Approach to Product Assortment and Shelf-SpaceAllocation, Journal of Retailing, Vol. 74, No. 1, 1998, pages 15-35,which discusses the integration of existing inventory-control models,product assortment models, and shelf-space allocation models to estimatethe demand for a product or products as a function of several factors,including the existing inventory level. The approach of Urban may beincorporated in the business planner system 1 to consider the change inconsumer demand that may occur due to changes in the details of theshelf-space handling of the product and related products during apromotion or during other times as well. Cross-elasticity may also beincluded the model, as Urban notes. The business planner system 1 mayinclude models based also on Urban's earlier work, T. L. Urban, “AMathematical Modeling Approach to Product Line Decisions,” Journal ofMarketing Research, Vol. 6, No. 1, 1969, pages 40-47, which provides ameans to determine which products should be included in a product linebased on a polynomial formula to model product demand as a function ofprice, advertising, and distribution, with main and cross-elasticitiesof marketing variables considered, with an iterative search routinerecommended for solution. Examples of studies showing means ofestimating cross-elasticity coefficients are reviewed by R. C. Curhan,“Shelf Space Allocation and Profit Maximization in Mass Retailing,”Journal of Marketing, Vol. 37, 1973, pages 54-60, and R. C. Curhan, “TheRelationship Between Shelf Space and Unit Sales in Supermarkets,”Journal of Marketing, Vol. 36, 1972, pages 406-12.

Cross-elasticity may be modeled to include asymmetry in demand such as achange in price of a high-priced brand will have a more dramatic effecton market share of a low-price brand than a change in price of thelow-price brand will have on the market share of the high-price brand.Cross-elasticity may also be modeled to include the neighborhood priceeffect: the observation that brands that are closer to each other inprice have larger cross-price effects than brands that are pricedfarther apart. Cross-elasticity may also be modeled to include otherknown effects, such as those disclosed, for example, by R. Sethuraman,V. Srinivasan, and D. Kim, “Asymmetric and Neighborhood Cross-PriceEffects: Some Empirical Generalizations,” Marketing Science, Vol. 18,No. 1, 1999. Such other effects include the role of the number ofcompeting products in a category, because cross-price effects tend to begreater when there are fewer competing brands in the product category.The effect of adding new products, or product proliferation, on demandmay also be modeled, for example, based on the work of B. L. Bayus andW. P. Putsis, Jr., “Product Proliferation: An Empirical Analysis ofProduct Line Determinants and Market Outcomes,” Marketing Science, Vol.18, No. 2, 1999. Bayus and Putsis propose a three-equation simultaneoussystem to estimate market outcomes of a firm's product-line decisions.In particular, they specify market share, price, and product line lengthequations, which are estimated by three-stage least squares. The effectof adding a new product line on the sales of other products may then beestimated.

The business planner system 1 uses cross elasticity factors to predictnot only how a price reduction or other promotion will increase sales ofthe promoted product, but how it will affect sales of other products aswell, including products in other categories, products from othermanufacturers, or products that might not seem related at first glance.The business planner system 1 includes deriving or obtaining crosselasticity coefficients, and then predicting sales lift and ROI for anyof the following: a) a specific product; b) a category of products froma single vendor; c) products from multiple vendors within a commoncategory; d) multiple products from multiple vendors. The main benefitis to the customer, but the manufacturer of the product being promotedbenefits from knowledge of how the promotion will affect the totalbottom line for the retailer, including increased sales of otherproducts from other vendors.

Other models may be used that incorporate cross-elasticity, includingthe work of M. Corstjens and P. Doyle, “A Model for Optimizing RetailSpace Allocations,” Management Science, Vol. 27, No. 7, 1981, pages822-33. Other modeling approaches of potential value when incorporatedinto the business planner system 1 include multi-item inventory modelsin general, and specific models such as the following:

-   -   The model of F. S. Zufryden, “Dynamic Programming Approach for        Product Selection and Supermarket Shelf-Space Allocation,”        Journal of the Operational Research Society, Vol. 37, No. 4,        1986, pages 413-22, which may be used to optimize the selection        of products allocated to shelf-space units in supermarkets,        accounting for space elasticity, cost of sales, and potential        demand-related marketing variables.    -   The model of Bultez and Naert, which is similar to that of        Corstjens and Doyle, but uses marginal analysis based on a        general theoretical formulation. They consider interdependencies        prevailing across and within product-groups. See A. Bultez        and P. Naert, “S.H.A.R.P.: Shelf Allocation for Retailers'        Profit,” Marketing Science, Vol. 73, No. 3, 1988, pages 211-31.    -   The model of Anderson and Amato for simultaneous analysis of        product assortment and shelf-space allocation problems.        See E. E. Anderson and H. N. Amata, “A Mathematical Model for        Simultaneously Determining the Optimal Brand-Collection and        Display-Area Allocation,” Operations Research, Vol. 22, No. 1,        1974, pages 13-21.    -   The model of Borin et al. as described in N. Borin, P. W.        Farris, and J. R. Freeland, “A Model for Determining Retail        Product Category Assortment and Shelf Space Allocation,”        Decision Sciences, Vol. 25, No. 3, 1994, pages 359-84, and in N.        Borin and P. W. Farris, “A Sensitivity Analysis of Retailer        Shelf Management Models, Journal of Retailing, Vol. 71, No. 2,        1995, pages 153-71, which integrates product assortment and        shelf-space allocation analyses including cross-elasticity        effects of substitute items and the effect on demand of products        when other products are not included in the assortment. A        solution strategy is suggested in the work as well (“simulated        annealing”).    -   The model of Baker and Urban for the effect of displayed        inventory on product demand, disclosed in R. C. Baker and T. L.        Urban, “A Deterministic Inventory System with an        Inventory-Level-Dependent Demand Rate,” Journal of the Operation        Research Society, Vol. 39, No. 9, 1988, pages 823-31. See        also R. C. Baker and T. L. Urban, “Single-Period Inventory        Dependent Demand Models,” Omega, Vol. 16, No. 6, 1988, pages        605-07.

Solution techniques for these models may include any of the ones taughtor recommended in the respective references, or more modern techniques,including neural networks, fuzzy logic systems, genetic algorithms, andthe like. Use of scanner data, consumer household data, or other datasources to provide empirical models for demand or other factors to beused in the business planner system 1 may be analyzed using suitablemethods including generalized linear models such as regression, as wellas loglinear, logit, and probit models. An example is discussed atwww2.chass.ncsu.edu/garson/pa765/logit.htm.

Historical sales information embodied in a memory device (e.g., apoint-of-sales database) can be mined by any suitable data mining methodfor relationships between promotions and products, includingcross-elasticity factors, sales lift as a function of market segment(demographic factors, etc.), impact of competitive promotions on vendorpromotions, and other factors that may not be readily apparent afterhuman scrutiny of the data. Exemplary methods for data mining are givenin Predictive Data Mining: A Practical Guide by Sholom M. Weiss andNitin Indurkhya (San Francisco: Morgan Kaufmann Publishers, 1997), ISBN1-55860-403-0. Data mining may be done according to CRISP-DM standardsin “CRISP-DM 1.0: Step-by-step Data Mining Guide” by P. Chapman et al.,at www.crisp-dm.org/CRISPWP-0800.pdf. Exemplary software tools for datamining include EDM (Enterprise Data-Miner) and DMSK (Data-Miner SoftwareKit), both available from Data-Miner Pty Ltd (Five Dock, Australia). Anysuitable data visualization or pattern detection tool can also beapplied, such as the OMNIVIZ-brand software system of OmniViz, Inc.(Maynard, Mass.).

Point-of-sale scanner data need not be limited to data scanned byoptical scanners, but may also include data obtained by other electronicscanning means, such as the use of radiofrequency identification (RFID)technology, in which minute “smart tags” attached to products can emit aradio signal conveying a product identification code that can identifythe item being purchased.

The business planner system 1 may also include models to predict how apromotion expands short-run and long-run category demand, which may bebased on work such as that disclosed by V. R. Nijs et al., “TheCategory-Demand Effects of Price Promotions,” Marketing Science, Vol.20, No. 1, 2001. Nijs et al. examine category-demand effects of consumerprice promotions across 560 consumer product categories over a 4-yearperiod. The data describe national sales in Dutch supermarkets and covera broad marketing mix, i.e., prices, promotions, advertising,distribution, and new-product activity. These methods focus on theestimation of main effects, such as the dynamic category expansiveimpact of price promotions, as well as the moderating effects ofmarketing intensity and competition on short- and long-run promotionaleffectiveness. Multivariate time-series analysis is used to disentangleshort- and long-run effects. First, these methods conduct a series oftests to determine whether or not category demand is stationary orevolving over time. The results are incorporated in the specification ofvector-autoregressive models with exogenous variables (VARX models). Theimpulse-response functions derived from these VARX models provideestimates of the short- and long-term effects of price promotions oncategory demand. These estimates, in turn, are used as dependentvariables in a series of second-stage regressions that assess theexplanatory power of marketing intensity and competition. Results aregiven in the form of empirical generalizations on the main effects ofprice promotions on category demand in the short and the long run andthrough statistical tests on how these effects change with marketingintensity and competition. The findings generate an overall picture ofthe power and limitations of consumer price promotions in expandingcategory demand.

Nijs et al. report that category demand is predominantly stationary,either around a fixed mean or a deterministic trend. Although the totalnet short-term effects of price promotions are generally strong, with anaverage elasticity of 2.21 and a more conservative median elasticity of1.75, they rarely exhibit persistent effects. Instead, the effectsdissipate over a time period lasting approximately ten weeks on average.By contrast, the successful introduction of new products into a categoryis more frequently associated with a permanent category demand increase.Thus, a model that relates demand to promotions may include factors thatdepend upon the nature of the product, such as a new product category, anew product in an existing category, an improved product, or an existingproduct, using a menu of tailored relationships between promotion anddemand that depend on the nature of the product.

The impact of advertising on sales may be predicted using any suitablemethod or model. Such predictions may also include estimates based onInternet advertising or other alternative media sources, using, forexample, the work of F. Zufryden, “Predicting Trial, Repeat, and SalesResponse from Alternative Media Plans,” Journal of AdvertisingResearch—Special Classic Issue, Vol. 40, No. 6, November/December 2000,as well as related works of Zufryden or others, including:

-   -   X. Drèze and F. Zufryden, “A Web-Based Methodology for Product        Design Evaluation and Optimisation,” Journal of the Operational        Research Society, October 1998, Vol. 49, No. 10, pp. 1034-43.    -   F. Zufryden, “A Model for Relating Advertising Media Exposures        to Purchase Incidence Patterns,” Management Science, Vol. 33,        No. 10, October 1987.    -   F. Zufryden, and G. Tellis, “Tackling the Retailer Decision        Maze: Which Brands to Discount, How Much, When and Why”,        Marketing Science, Vol. 14, No. 3, 1995.    -   F. Zufryden and J. H. Pedrick), “Measuring the Competitive        Effects of Advertising Media Plans,” Journal of Advertising        Research, November/December 1993.    -   Zufryden, Fred, “The WNBD: A Stochastic Model Approach for        Relating Explanatory Variables to Consumer Purchase Dynamics,”        the International Journal of Research in Marketing, Vol. 8,        1991.

The business planner system 1 may be integrated with ContinuousReplenishment systems. Continuous Replenishment refers to the practiceof partnering between distribution channel members that changes thetraditional replenishment process from distributor-generated purchaseorders, based on economic order quantities, to the replenishment ofproducts based on actual and forecasted product demand. Inventory levelsand operating costs may be reduced by having products delivered on afrequent, as-needed basis. Consumer demand based on scan data and salesforecasts, including business planner system forecasts for promotions,may drive warehouse replenishment orders and shipping.

FIG. 7 shows one embodiment of a computer network 140 that may be usedfor the business planner system 1 of the present invention. As depicted,a retailer 154 may use the retailer's computer systems 180 to access thecomputer network 140 through a firewall 160 (serving as a gate) undercontrol of the manufacturer. A user on behalf of the retailer 154 entersa URL to access a secure Web site. The URL request goes through thefirewall 160 to a first router 184 (e.g., a CISCO-brand router) whereeither a primary DNS (domain name server) 188 or secondary DNS 190determines the IP address to be used for the requested URL. A signal isthen sent to the Internet application server 192, which generates asignal to create a Web page display. The signal is routed back to thecomputer 180 of the retailer 154 such that a Web page is displayed on amonitor 182. The displayed Web page requires the user to log in using auser ID and password. When the user ID and password are entered, thatinformation is routed again through the firewall 160 to a second router186 that directs the information to an ID/password authentication server194 (e.g., an SQL server). If a valid user ID and password have beenentered, a welcome page for the business planner system 1 is thendisplayed (e.g., a signal is sent to the Internet application server 192which then sends a signal back to the computer 180 of the retailer todisplay a business planner system Web page).

The welcome page displayed after logging in is unique to the retailer154 (i.e., it may have functions and an appearance customized for thespecific retailer 154 or even the specific user or for the department inwhich the user is employed). The page may provide access to additionalpages that contain information unique to the retailer 154, such aswholesale price information, access to terms of contracts and otheragreements, details of past promotions, and plans for future promotionsor product launches. Both historical information and forecasts may beprovided regarding sales, shipment schedules, promotions, profitability,and so forth. Information about promotions and the information requiredto forecast profitability and sales lift may be stored on a businessplanner server 204, which may be a single server for multiple retailersand/or product lines, or may comprise multiple networked servers eachdedicated to a particular product, product category, retailer, orcollection of retailers. Programs for integrating planned promotions fora plurality of retailers and/or for a plurality of products may behosted on the business planner server 204 or an operations server 198.

Information regarding planned production, wholesale costs, manufacturerprofitability, and the like may be stored on the operations server 198.Operations 152, which may include mills or other business groups of themanufacturer, may use their computer systems 210 to provide informationregarding production quantities and other factors to the operationsserver 198, and may access information regarding sales lift orpromotions from the business planner server 204.

Using the retailer's computer systems 180 with access to the businessplanner system 1, the logged-in user may then enter various scenarios todetermine how a proposed promotion may affect sales and profitability,or may explore cross-elasticity effects, for example. A scenario, suchas a contemplated temporary price reduction for a given product during aspecific time period, may be entered, resulting in a signal routedthrough the firewall 160 to the business planner server 204 where saleslift and retailer profit or other output variables may be calculated andthen displayed on a Web page based on a signal sent back to the computer180 of the user via the firewall 160.

The information accessible to the retailer 154 may be restrictedrelative to the information accessible to sales personnel 156, who maybe able to examine the effect of promotions for a plurality of retailers154 and may also be able to observe displays via a computer system 181on a monitor 183 showing a broad range of information such asprofitability to the manufacturer as well as to an individual retailer,and optionally the total predicted sales volume integrated acrossmultiple retailers 154.

In receiving inputs from users such as employees of the retailer 154 orsales personnel 156 employed by the manufacturer, security means may beemployed to verify the identity of the user. For example, an electronicsignature may be obtained, or biometrics may be employed. For futureauditing, the user ID, the electronic signature, and the requestedinformation may all be stored (e.g., on the business planner server 196or other server) to provide an audit trail.

In one embodiment, the business planner system 1 may include electronicmeans for placing and paying for an order from the manufacturer via thecomputer network 140.

The business planner system 1 is overseen by an administrator 200, whouses a computer system 208 to access the business planner server and itsdatabases via a proxy server 202. Administrators 200 may modify databasecontents, enter administrative information to document changes forpurposes of future auditing, modify accounts of both retailers 154 andsales personnel 156 (e.g., via the connection shown between the proxyserver 202 and the ID/password authentication server 194), and so forth.

The network as accessed by the retailer 154 or sales personnel 156 maybe a virtual private network (VPN), and may be based on MultiprotocolLabel Switching (MPLS) technology, including embodiments discussed by M.Smetannikov in “MPLS VPNs Controversial,” Interactive Week, Oct. 15,2001, p. 20, including the ExpressRoute service of Global Crossing(Beverly Hill, Calif.).

Integration of projected sales volume with supply chain systems isuseful in ensuring the success of planned promotions, integratingproduction plans with marketing plans and combined sales staffactivities. Use of the business planner system 1 allows sales staff inthe field to understand the financial impact of their plannedpromotions, and to intelligently select from a plurality of promotionoptions based on the predicted returns. Further, sales staff may beprovided with a tool to run multiple “what if” scenarios to show thefinancial returns to retailers for proposed promotions. Thus, for thefirst time, sales staffs are given tools to predict the financial impactof a planned promotion on both the manufacturer and the retailer, andthe tools to carry out the planned promotion in a manner thatcorresponds to corporate production and marketing plans and objectives.

The business planner system 1 may be adapted for multinational or globaluse by adding translation modules to allow input and operation inmultiple languages. The system may be configured to support anycurrency. Conversion to a standard currency may be accomplished throughuse of SAP BW or any other suitable data warehousing tool.

Use of the business planner system's profit-prediction tools may allowthe manufacturer to maximize the return on funds allocated forpromotions. A given promotion budget may be allocated between markets orgeographical regions in a manner that optimizes profits, and a givenquantity of funds for promotions in a given region or market may beoptimized for maximum profits.

While the invention has been described in conjunction with severalspecific embodiments, it is to be understood that many alternatives,modifications and variations will be apparent to those skilled in theart in light of the foregoing description. Accordingly, this inventionis intended to embrace all such alternatives, modifications andvariations that fall within the spirit and scope of the appended claims.Further, while the methods disclosed herein have been described andshown with reference to particular acts performed in a particular order,it will be understood that these acts may be combined, sub-divided, orre-ordered to form an equivalent method without departing from theteachings of the present invention. Accordingly, unless specificallyindicated herein, the order and grouping of the acts are not limitationsof the present invention.

1. A method for predicting the profit attributable to a proposed salespromotion of a product, wherein the product has a wholesale price and amanufacturing cost per unit sales, the method comprising using acomputer to perform the following steps: establishing a base volume forsales of the product in the absence of promotions, wherein theestablishing act includes performing non-parametric regression ofhistorical shipping data; determining a sales lift for a plurality ofsingle promotions; correlating the sales lift with promotion informationto provide a sales lift model; proposing a promotion having a cost perunit sales for a promotion time period and having a planned sale pricefor the product; applying the sales lift model to the proposed promotionto predict sales of the product for the promotion time period;calculating manufacturer profit based upon the product's predictedsales, cost per unit sales for promotion, wholesale price, andmanufacturing cost per unit sales during the promotion time period. 2.The method of claim 1, wherein the promotion is selected from the groupconsisting of a temporary price reduction, a distributed couponcampaign, an in-store coupon campaign, a loyalty card promotion, arebate, and an advertised price reduction.
 3. The method of claim 1,wherein the promotion is selected from the group consisting of asweepstakes, a free gift offered with purchase of the product, and anattached coupon for reduced cost for another service or product.
 4. Themethod of claim 1, further comprising determining the expected costincurred by customer forward buying based upon past forward buyingbehavior.
 5. The method of claim 1, wherein the establishing actincludes analyzing point-of-sale data from at least one time periodlacking a promotion.
 6. The method of claim 1, wherein the establishingact includes analyzing product shipping data.
 7. The method of claim 1,wherein the establishing act includes Kalman filtering historicalshipping data.
 8. The method of claim 1, wherein the establishing actfurther comprises: obtaining a time series of shipping data; normalizingthe time series; truncating large peaks in the time series associatedwith promotions; applying a numerical tool to the time series selectedfrom a dynamic linear model or a Kalman filter; performing regression onthe time series to obtain regression estimates; smoothing the regressionestimates; and applying the regression estimates to predict base volumefor the promotion time period.
 9. The method of claim 1, furthercomprising modeling cross elasticity between products.
 10. The method ofclaim 1, further comprising modeling cross elasticity between brands.11. The method of claim 1, further comprising modeling cross elasticitybetween products from different vendors.
 12. The method of claim 1,further comprising providing historical point-of-sale data for theproduct or a related product category during a time period including aplurality of single promotions.
 13. A method for predicting the profitattributable to a proposed sales promotion of a product, wherein theproduct has a wholesale price and a manufacturing cost per unit sales,the method comprising using a computer to perform the following steps:establishing a base volume for sales of the product in the absence ofpromotions, wherein the establishing act includes Kalman filteringhistorical shipping data; determining a sales lift for a plurality ofsingle promotions; correlating the sales lift with promotion informationto provide a sales lift model; proposing a promotion having a cost perunit sales for a promotion time period and having a planned sale pricefor the product; applying the sales lift model to the proposed promotionto predict sales of the product for the promotion time period;calculating manufacturer profit based upon the product's predictedsales, cost per unit sales for promotion, wholesale price, andmanufacturing cost per unit sales during the promotion time period. 14.A method for predicting the profit attributable to a proposed salespromotion of a product, wherein the product has a wholesale price and amanufacturing cost per unit sales, the method comprising using acomputer to perform the following steps: establishing a base volume forsales of the product in the absence of promotions, wherein theestablishing act further comprises: obtaining a time series of shippingdata; normalizing the time series; truncating large peaks in the timeseries associated with promotions; applying a numerical tool to the timeseries selected from a dynamic linear model or a Kalman filter;performing regression on the time series to obtain regression estimates;smoothing the regression estimates; and applying the regressionestimates to predict base volume for the promotion time period;determining a sales lift for a plurality of single promotions;correlating the sales lift with promotion information to provide a saleslift model; proposing a promotion having a cost per unit sales for apromotion time period and having a planned sale price for the product;applying the sales lift model to the proposed promotion to predict salesof the product for the promotion time period; calculating manufacturerprofit based upon the product's predicted sales, cost per unit sales forpromotion, wholesale price, and manufacturing cost per unit sales duringthe promotion time period.